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Director of BSA/AML

Manage ongoing customer due diligence program

Enhances✓ Available Now

What You Do Today

Ensure high-risk customers receive enhanced due diligence, beneficial ownership information stays current, and risk ratings are updated based on evolving activity patterns.

AI That Applies

Dynamic risk scoring — AI continuously re-scores customer risk based on transaction behavior, news events, and network analysis instead of relying on static annual reviews.

Technologies

How It Works

The system ingests transaction behavior as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Risk ratings update in real-time. A customer who was medium-risk last year gets flagged for review because their transaction pattern shifted to match known money laundering typologies.

What Stays

Enhanced due diligence — actually talking to the customer, verifying the business purpose, making the keep-or-exit decision — is human work.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for manage ongoing customer due diligence program, understand your current state.

Map your current process: Document how manage ongoing customer due diligence program works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Enhanced due diligence — actually talking to the customer, verifying the business purpose, making the keep-or-exit decision — is human work. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Verafin tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long manage ongoing customer due diligence program takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your Chief Compliance Officer

What are the top 5 reasons customers contact us, and which of those could be resolved without a human?

They set the risk appetite for AI adoption in regulated processes

your legal counsel

How do we currently measure service quality, and would AI-assisted responses change that measurement?

AI in compliance creates new regulatory interpretation questions

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.